No abstract

Goat's a S1 -casein (CSN1S1) polymorphism has a significant effect on milk protein and lipid composition, which affects the nutritional quality and technological properties of milk. Moreover, this polymorphism has a large impact on the morphology of mammary epithelial cells. To explore the metabolic pathways modulated in relation to this polymorphism, we compared the mammary gene expression profiles of two groups of lactating goats carrying either two reference or two defective alleles, using a bovine oligonucleotide microarray representing 8379 genes. We identified 41 differentially expressed genes between the two genotype groups. In particular, we showed a downregulation of two key lipogenic genes encoding fatty acid synthase and glycerol-3-phosphate acyltransferase in agreement with the low fat concentration associated with CSN1S1 deficiency. In addition, this study highlights changes in the expression level of several genes known to influence membrane fluidity, cell-cell interaction or chromatin organization. Our results open up new fields of investigation on structural modifications associated with CSN1S1 deficiency that could affect mammary gland function.

This study was conducted with the aim of optimizing the experimental design of array experiments. We compared two image analysis and normalization procedures prior to data analysis using two experimental designs. For this, RNA samples from Charolais steers Longissimus thoracis muscle and subcutaneous adipose tissues were labeled and hybridized to a bovine 8,400 oligochip either in triplicate or in a dye-swap design. Image analysis and normalization were processed by either GenePix/MadScan or ImaGene/GeneSight. Statistical data analysis was then run using either the SAM method or a Student’s t-test using a multiple test correction run on R 2.1 software. Our results show that image analysis and normalization procedure had an impact whereas the statistical methods much less influenced the outcome of differentially expressed genes. Image analysis and data normalization are thus an important aspect of microarray experiments, having a potentially significant impact on downstream analyses such as the identification of differentially expressed genes. This study provides indications on the choice of raw data preprocessing in microarray technology.